Energy storages such as batteries are combined with power generators to constitute energy supply systems which supply electric power to various equipment. Such energy supply systems can operate without electric power from other sources such as grids (i.e., commercial power supplies) and renewable power sources (also known as “renewables”). Alternatively, energy supply systems can be supplied with power from grids and/or renewables. In some cases, grids are unreliable grids which often suffer blackout. Recently, energy storage systems are becoming cheaper, more compact and reliable, they can help to improve overall efficiency of energy supply systems by increasing the generator loading. However, various factors affect the efficiency of energy supply systems, and therefore, an energy management system which aids more efficient operation of the energy supply system is required.
The goal of the energy management system is to reduce cost and fuel consumption of the generator and keep generator wear within reasonable bounds by use of intelligent prediction, optimization and control. Also the inclusion of a renewable power source into the energy supply system can be managed by the energy management system.
A typical example would be the energy management for an energy supply system for a base transceiver station (BTS) which is equipped with a battery and a Diesel generator and connected with a grid subjected to blackouts, as it occurs typically in rural areas of developing countries. Typically in such applications, when the grid is available, the equipment such as BTS equipment and air conditioning take their energy from the grid, but during blackout, the energy for the load is taken from the energy storage and the Diesel generator (and sometimes also renewables such as PV (photo voltaic) generation and/or wind generation). Optimal charge/discharge cycles for the energy storage and appropriate on/off commands for the Diesel generator during off grid time lead to optimal operation of the energy supply.
Examples of the energy storage-based energy supply systems with no or sporadic grid supply are described in related art references [NPL1], [NPL2]. These systems do not use load prediction, and in the case that renewable generation is used, they do not use renewable generation prediction. Especially, the systems neither use blackout duration probability function prediction technology nor provide means to include uncertainty in the optimization of the charge/discharge pattern and the generator on/off command.
Therefore, the systems of the related arts, fuel and cost optimality by choosing the right charge/discharge pattern of the battery and the generator on/off command cannot be achieved. The systems of the related arts do not use the available information as best as possible.
The problem with related-art systems is that, for example, it is not guaranteed that at the end of the blackout the energy storage (typically, a battery) is not unnecessarily charged and that the generator runs most efficiently considering the optimal generator loading and a low number of generator starts (it depends on the type of generator if the latter is an issue). The unnecessary charging of a battery results in a loss of efficiency, since the battery can be charged at lower cost with electric grid energy.
The related-art system described in [NPL1] relies on a priority based switching logic without prediction and optimization, where the different power sources are given priorities. For example, priorities of 1, 2, 3 and 4 are assigned to renewables, a battery, a grid and a generator, respectively. However, the system is not able to achieve fuel or cost optimality.
In the method described in [NPL2], during blackout, the battery is fully discharged and charged and this cycle repeated. However, also this method does not achieve full optimality.
A system described in [PL1] which is provided with a PV generator and a battery uses day-ahead prediction and decides to charge or not to charge the battery during the night depending if the needed energy of the loads is higher or lower than the predicted generated energy by the renewable source. However, this system does not foresee the connection of a generator with PDE as, for example, a Diesel engine generator represents.
A system described in [PL2] relates to an electric vehicle (EV) including a fuel cell (generator), a motor (load) and a battery and controls the EV to improve overall efficiency. However, the solution is not applicable to the type of PDE generator, since for fuel cell more loading reduces efficiency. Additionally, the system of [PL2] works with fixed charging and discharging limits, which would amount to suboptimal operation in the general application.
A certain hardware topology for the connection of a grid, a renewable power source and a battery is proposed in [PL3]. However, the proposal in [PL3] does not consider blackouts or non linear generators.
Related-art systems that deal with blackout prediction are described in, for example, [PL4]-[PL8]. These systems either predict the concrete start time of the blackout or calculate the blackout probability of a certain time instant but do not consider the blackout duration explicitly. In [PL9], only a planned blackout, i.e., a blackout whose start time and end time are known, is considered, so it is assumed that the blackout information is perfectly known. This is generally not the case.
By the way, for some type of applications, the knowledge of the concrete time instant of the blackout is not necessary for optimal operation. For optimal operation of a energy storage and a generator the blackout duration is important. Knowing the blackout duration, and the future load and renewable generation allows the determination of optimal charge/discharge cycles of the pattern together with the right on/off command for the generator.